Generally, data analytics relates to the process of drawing information or conclusions from data. Data analytics relates, for example, to data mining, model development, predictive data modeling, and the like. An analytics platform provides tools so that users of the platform can analyze the data and perform data analytics.
There are many uses for an analytics platform. Users of an analytics platform can use the tools and the data sets of the analytics platform to perform investigations, develop models, make predictions, and support policies. An entity or user, for example, may use an analytics platform to evaluate business data in order to help the business make better decisions or to model the behavior of their customers or to predict sales. An analytics platform may be used to investigate theories and correlations and for other reasons.
Even though analytics platforms can provide very powerful tools, there are still weaknesses that limit their effectiveness. For example, analytics platforms often store data sets in a large repository. Once the data sets are imported and stored in the repository, some of the data sets may become unused over time. Data sets tend to become lost in the sense that users may not be aware of their existence and potential use. In addition, relationships between data sets are difficult to understand and track and the ability to discover relevant data and tools for analytic projects is cumbersome. Systems and methods are needed to facilitate the way in which data sets and other assets in analytics platforms are accessed and used.